1. Field of the Invention
The present invention relates to face recognition systems and particularly, to a system and method for performing face recognition using proportions of the learned model.
2. Discussion of the Prior Art
Existing face recognition systems attempt to recognize an unknown face by matching against prior instances of that subject's face(s). This is typically performed by training a classifier against prior instances of a subject's face and then using the trained classifier to identify the subject by matching against new instances of that subjects face. As known, training a classifier involves learning a model of the subject's face. Existing systems use the whole model during classification.
While the ultimate goal in the design of any pattern recognition system is to achieve the best possible classification (predictive) performance, this objective traditionally has led to the development of different classification schemes for any pattern recognition problem to be solved. The results of an experimental assessment of the different designs would then be the basis for choosing one of the classifiers (model selection) as a final solution to the problem. It has been observed in such design studies, that although one of the designs would yield the best performance, the sets of patterns misclassified by the different classifiers would not necessarily overlap as recognized by Kittler J., Hatef, H. and Duin, R. P. W. in the reference entitled “Combining Classifiers, in Proceedings of the 13th International Conference on pattern Recognition”, Vol. II, pp. 897-901, Vienna, Austria, 1996. This suggested that different classifier designs potentially offered complementary information about the patterns to be classified, which could be harnessed to improve the overall performance.
It had been a common practice in the application of neural networks to train many different candidate networks and then select the best, on the basis of performance on an independent validation set for instance, and to keep only this network and to discard the rest. There are two disadvantages with such an approach. First, all of the effort involved in training the remaining networks is wasted. Second, the generalization performance on the validation set has a random component due to the noise in the data, and so the network which had best performance on the validation set might not be the one with the best performance on new or unseen test data. These drawbacks can be overcome by combining the networks together to form a committee of networks. The importance of such an approach is that it can lead to significant improvements in the predictions on new data, while involving little additional computational effort. In fact the performance of a committee can be better than the performance of the best single network used in isolation as recognized by Bishop C. M., in the reference entitled “Neural Networks for Pattern Recognition,” Oxford Press, Oxford, UK, pp. 364-377, 1997.
In order to recognize faces, recognition systems have employed multiple classifiers each trained on profiles of an individual face. On presentation of a probe (test image), the probe is matched with each of the learned model and the scores obtained from each classifier are used up to arrive at a consensus decision. An obvious disadvantage of training multiple classifiers is that a lot of time and space is wasted in training and storing the model files.
It would be highly desirable to provide a face recognition system and methodology whereby instead of having multiple classifiers trained on various profiles of an individual face, a single classifier may be trained on either a frontal face or multiple profiles of an individual's face.
It would further be highly desirable to provide a face recognition system and method wherein proportions of a subject's model is implemented and used to match against different proportions of a subject's face. That is, during testing, an unknown facial image is identified by matching different proportions of the learned model and the unknown facial image.